Albert S. Berahas
Albert S. Berahas
Postdoctoral Research Fellow, Lehigh University
Verified email at u.northwestern.edu - Homepage
Title
Cited by
Cited by
Year
A multi-batch L-BFGS method for machine learning
AS Berahas, J Nocedal, M Takác
Advances in Neural Information Processing Systems, 1055-1063, 2016
502016
An investigation of Newton-sketch and subsampled Newton methods
AS Berahas, R Bollapragada, J Nocedal
Optimization Methods and Software, 1-20, 2020
442020
Balancing communication and computation in distributed optimization
AS Berahas, R Bollapragada, NS Keskar, E Wei
IEEE Transactions on Automatic Control 64 (8), 3141-3155, 2018
272018
adaqn: An adaptive quasi-newton algorithm for training rnns
NS Keskar, AS Berahas
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2016
242016
Derivative-free optimization of noisy functions via quasi-Newton methods
AS Berahas, RH Byrd, J Nocedal
SIAM Journal on Optimization 29 (2), 965-993, 2019
102019
A robust multi-batch l-bfgs method for machine learning
AS Berahas, M Takáč
Optimization Methods and Software 35 (1), 191-219, 2020
92020
Quasi-newton methods for deep learning: Forget the past, just sample
AS Berahas, M Jahani, M Takáč
arXiv preprint arXiv:1901.09997, 2019
92019
Sparse representation and least squares-based classification in face recognition
M Iliadis, L Spinoulas, AS Berahas, H Wang, AK Katsaggelos
2014 22nd European Signal Processing Conference (EUSIPCO), 526-530, 2014
82014
A theoretical and empirical comparison of gradient approximations in derivative-free optimization
AS Berahas, L Cao, K Choromanski, K Scheinberg
arXiv preprint arXiv:1905.01332, 2019
72019
Scaling up quasi-newton algorithms: Communication efficient distributed sr1
M Jahani, M Nazari, S Rusakov, AS Berahas, M Takáč
arXiv preprint arXiv:1905.13096, 2019
22019
Nested Distributed Gradient Methods with Adaptive Quantized Communication
AS Berahas, C Iakovidou, E Wei
58th IEEE Conference on Decision and Control (CDC), 1519-1525, 2019
22019
Multi-model robust error correction for face recognition
M Iliadis, L Spinoulas, AS Berahas, H Wang, AK Katsaggelos
2016 IEEE International Conference on Image Processing (ICIP), 3229-3233, 2016
22016
Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise
AS Berahas, L Cao, K Scheinberg
arXiv preprint arXiv:1910.04055, 2019
12019
Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization
AS Berahas, L Cao, K Choromanski, K Scheinberg
arXiv preprint arXiv:1905.13043, 2019
2019
Limited-Memory BFGS with Displacement Aggregation
AS Berahas, FE Curtis, B Zhou
arXiv preprint arXiv:1903.03471, 2019
2019
Sampled Quasi-Newton Methods for Deep Learning
AS Berahas, M Jahani, M Takác
OPT 2019: Optimization for Machine Learning Workshop (NeurIPS 2019), 2019
2019
Methods for Large Scale Nonlinear and Stochastic Optimization
AS Berahas
Northwestern University, 2018
2018
adaQN: An Adaptive Quasi-Newton Algorithm for Training RNNs
N Shirish Keskar, AS Berahas
arXiv preprint arXiv:1511.01169, 2015
2015
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